A FPGA-based neural accelerator for small IoT devices

Seongmin Hong; Yongjun Park

doi:10.1109/ISOCC.2017.8368903

A FPGA-based neural accelerator for small IoT devices

Seongmin Hong, Yongjun Park

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

Neural network has been widely used for various applications. While most of previous approaches tried to use large neural networks such as convolutional neural network (CNN) and deep neural network (DNN), these heavy models are hardly adapted to IoT(internet of things) platforms due to their limited resources. This work proposes a compact neural network accelerator for IoT devices. Our design shows 11.95 GOP/s total throughput and 413.99mW power consumption with 98.04% accuracy.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2017, ISOCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	294-295
Number of pages	2
ISBN (Electronic)	9781538622858
DOIs	https://doi.org/10.1109/ISOCC.2017.8368903
Publication status	Published - 2018 May 29
Event	14th International SoC Design Conference, ISOCC 2017 - Seoul, Korea, Republic of Duration: 2017 Nov 5 → 2017 Nov 8

Publication series

Name	Proceedings - International SoC Design Conference 2017, ISOCC 2017

Other

Other	14th International SoC Design Conference, ISOCC 2017
Country/Territory	Korea, Republic of
City	Seoul
Period	17/11/5 → 17/11/8

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/ISOCC.2017.8368903

Cite this

@inproceedings{c17f3a199e71433793736253005f3fb4,

title = "A FPGA-based neural accelerator for small IoT devices",

abstract = "Neural network has been widely used for various applications. While most of previous approaches tried to use large neural networks such as convolutional neural network (CNN) and deep neural network (DNN), these heavy models are hardly adapted to IoT(internet of things) platforms due to their limited resources. This work proposes a compact neural network accelerator for IoT devices. Our design shows 11.95 GOP/s total throughput and 413.99mW power consumption with 98.04% accuracy.",

author = "Seongmin Hong and Yongjun Park",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th International SoC Design Conference, ISOCC 2017 ; Conference date: 05-11-2017 Through 08-11-2017",

year = "2018",

month = may,

day = "29",

doi = "10.1109/ISOCC.2017.8368903",

language = "English",

series = "Proceedings - International SoC Design Conference 2017, ISOCC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "294--295",

booktitle = "Proceedings - International SoC Design Conference 2017, ISOCC 2017",

address = "United States",

}

Hong, S & Park, Y 2018, A FPGA-based neural accelerator for small IoT devices. in Proceedings - International SoC Design Conference 2017, ISOCC 2017. Proceedings - International SoC Design Conference 2017, ISOCC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 294-295, 14th International SoC Design Conference, ISOCC 2017, Seoul, Korea, Republic of, 17/11/5. https://doi.org/10.1109/ISOCC.2017.8368903

A FPGA-based neural accelerator for small IoT devices. / Hong, Seongmin; Park, Yongjun.
Proceedings - International SoC Design Conference 2017, ISOCC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 294-295 (Proceedings - International SoC Design Conference 2017, ISOCC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A FPGA-based neural accelerator for small IoT devices

AU - Hong, Seongmin

AU - Park, Yongjun

PY - 2018/5/29

Y1 - 2018/5/29

N2 - Neural network has been widely used for various applications. While most of previous approaches tried to use large neural networks such as convolutional neural network (CNN) and deep neural network (DNN), these heavy models are hardly adapted to IoT(internet of things) platforms due to their limited resources. This work proposes a compact neural network accelerator for IoT devices. Our design shows 11.95 GOP/s total throughput and 413.99mW power consumption with 98.04% accuracy.

AB - Neural network has been widely used for various applications. While most of previous approaches tried to use large neural networks such as convolutional neural network (CNN) and deep neural network (DNN), these heavy models are hardly adapted to IoT(internet of things) platforms due to their limited resources. This work proposes a compact neural network accelerator for IoT devices. Our design shows 11.95 GOP/s total throughput and 413.99mW power consumption with 98.04% accuracy.

UR - http://www.scopus.com/inward/record.url?scp=85048867939&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048867939&partnerID=8YFLogxK

U2 - 10.1109/ISOCC.2017.8368903

DO - 10.1109/ISOCC.2017.8368903

M3 - Conference contribution

AN - SCOPUS:85048867939

T3 - Proceedings - International SoC Design Conference 2017, ISOCC 2017

SP - 294

EP - 295

BT - Proceedings - International SoC Design Conference 2017, ISOCC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International SoC Design Conference, ISOCC 2017

Y2 - 5 November 2017 through 8 November 2017

ER -

A FPGA-based neural accelerator for small IoT devices

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this